[fix] Fixed kvcache offload bugs.

nanovllm/config.py

@@ -22,6 +22,7 @@ class Config:
     offload_policy: str = "lru"  # "lru", "fifo", or full class path
     num_transfer_streams: int = 4  # Number of CUDA streams for async transfers
     num_gpu_blocks: int = -1  # User-specified GPU blocks count, -1 = auto (use max available)
+    num_prefetch_blocks: int = 2  # Prefetch区的block数量，用于三区域GPU Buffer设计
 
     # Computed fields for offload (set in __post_init__ or by ModelRunner)
     num_gpu_kvcache_blocks: int = -1

nanovllm/engine/model_runner.py

@@ -148,19 +148,39 @@ class ModelRunner:
             dtype=hf_config.torch_dtype,
         )
 
-        # Log KV cache allocation info
+        # Log KV cache allocation info with detailed per-token breakdown
         gpu_memory_mb = config.num_gpu_kvcache_blocks * block_bytes / (1024 ** 2)
         cpu_memory_mb = config.num_cpu_kvcache_blocks * block_bytes / (1024 ** 2)
         total_memory_mb = gpu_memory_mb + cpu_memory_mb
 
+        # Calculate per-token KV cache usage
+        # KV per token = 2 (K+V) * num_layers * kv_heads * head_dim * dtype_size
+        dtype_size = 2 if hf_config.torch_dtype in [torch.float16, torch.bfloat16] else 4
+        per_token_kv_bytes = 2 * hf_config.num_hidden_layers * num_kv_heads * head_dim * dtype_size
+        per_token_kv_kb = per_token_kv_bytes / 1024
+
+        logger.info(
+            f"KV Cache per-token: {per_token_kv_kb:.2f}KB "
+            f"(2 * {hf_config.num_hidden_layers}layers * {num_kv_heads}kv_heads * {head_dim}head_dim * {dtype_size}bytes)"
+        )
+        logger.info(
+            f"KV Cache per-block: {block_bytes / (1024**2):.2f}MB "
+            f"({per_token_kv_kb:.2f}KB * {self.block_size}tokens)"
+        )
+
         if config.enable_cpu_offload:
+            ping_size = config.num_gpu_kvcache_blocks // 2
+            tokens_per_ping = ping_size * self.block_size
             logger.info(
                 f"KV Cache allocated (Ping-Pong mode): "
                 f"GPU={config.num_gpu_kvcache_blocks} blocks ({gpu_memory_mb:.1f}MB), "
                 f"CPU={config.num_cpu_kvcache_blocks} blocks ({cpu_memory_mb:.1f}MB), "
-                f"Total={total_memory_mb:.1f}MB, "
+                f"Total={total_memory_mb:.1f}MB"
-                f"block_size={self.block_size}, "
+            )
-                f"ping_size={config.num_gpu_kvcache_blocks // 2}"
+            logger.info(
+                f"Ping-Pong config: ping_size={ping_size} blocks, "
+                f"tokens_per_chunk={tokens_per_ping}, "
+                f"block_size={self.block_size}"
             )
         else:
             logger.info(
@@ -392,12 +412,12 @@ class ModelRunner:
 
     def _should_use_pingpong(self, seqs: list[Sequence], is_prefill: bool) -> bool:
         """
-        Check if Ping-Pong mode should be used.
+        Check if 三区域 mode should be used.
 
-        Use Ping-Pong when:
+        Use 三区域 when:
         - CPU offload is enabled
         - There are blocks on CPU (either allocated there or offloaded)
-        - Sequence exceeds GPU capacity
+        - Sequence exceeds GPU Compute区 capacity
         """
         if not hasattr(self.kvcache_manager, 'offload_engine'):
             return False
@@ -409,12 +429,12 @@ class ModelRunner:
             # Check if any blocks are on CPU
             cpu_blocks, _ = self.kvcache_manager.get_all_cpu_blocks(seq)
             if cpu_blocks:
-                # Has CPU blocks - use Ping-Pong
+                # Has CPU blocks - use 三区域
                 return True
 
-            # Check if sequence needs more blocks than GPU can hold
+            # Check if sequence needs more blocks than GPU Compute区 can hold
-            ping_size = self.kvcache_manager.offload_engine.ping_size
+            compute_size = self.kvcache_manager.offload_engine.num_compute_blocks
-            if seq.num_blocks > ping_size:
+            if seq.num_blocks > compute_size:
                 # Needs chunked processing
                 return True
 
@@ -610,29 +630,28 @@ class ModelRunner:
 
     def run_pingpong_prefill(self, seqs: list[Sequence]) -> list[int]:
         """
-        Run prefill with Ping-Pong dual buffer (CPU is primary storage).
+        Run prefill with 三区域 GPU buffer (CPU is primary storage).
 
         Flow:
         1. All blocks are allocated to CPU (primary storage)
-        2. Process tokens in chunks using Ping/Pong GPU buffers
+        2. Process tokens in chunks using Compute区 GPU buffer
-        3. After each chunk, offload from GPU to CPU
+        3. After each chunk, offload from Compute区 to CPU
-        4. Alternate between Ping and Pong buffers
+        4. Prefetch区 用于加载 previous KV（如果有的话）
         """
         import sys
 
-        assert len(seqs) == 1, "Ping-Pong prefill only supports single sequence"
+        assert len(seqs) == 1, "三区域 prefill only supports single sequence"
         seq = seqs[0]
 
         offload_engine = self.kvcache_manager.offload_engine
-        ping_size = offload_engine.ping_size
+        compute_size = offload_engine.num_compute_blocks
-        tokens_per_chunk = ping_size * self.block_size
+        tokens_per_chunk = compute_size * self.block_size
 
         total_tokens = len(seq)
-        print(f"[Ping-Pong Prefill] Starting: {total_tokens} tokens, "
+        print(f"[三区域 Prefill] Starting: {total_tokens} tokens, "
-              f"ping_size={ping_size} blocks, chunk={tokens_per_chunk} tokens",
+              f"compute_size={compute_size} blocks, chunk={tokens_per_chunk} tokens",
               file=sys.stderr)
 
-        current_buffer = "ping"
         chunk_num = 0
         logits = None
         processed_tokens = 0
@@ -651,15 +670,13 @@ class ModelRunner:
             end_block_idx = (chunk_end + self.block_size - 1) // self.block_size
             num_blocks = end_block_idx - start_block_idx
 
-            print(f"[Ping-Pong Prefill] Chunk {chunk_num}: tokens {chunk_start}-{chunk_end}, "
+            print(f"[三区域 Prefill] Chunk {chunk_num}: tokens {chunk_start}-{chunk_end}, "
-                  f"blocks {start_block_idx}-{end_block_idx-1}, buffer={current_buffer}",
+                  f"blocks {start_block_idx}-{end_block_idx-1}, "
+                  f"compute_slots={offload_engine.compute_slots[:num_blocks]}",
                   file=sys.stderr)
 
-            # Get GPU slots for this chunk (Ping or Pong buffer)
+            # Get GPU slots for this chunk (使用 Compute区)
-            if current_buffer == "ping":
+            gpu_slots = offload_engine.compute_slots[:num_blocks]
-                gpu_slots = offload_engine.ping_slots[:num_blocks]
-            else:
-                gpu_slots = offload_engine.pong_slots[:num_blocks]
 
             # Prepare inputs
             input_ids, positions = self._prepare_pingpong_chunk(
@@ -678,24 +695,19 @@ class ModelRunner:
                 logical_id = seq.block_table[i]
                 self.kvcache_manager.prefilled_blocks.add(logical_id)
 
-            # Offload this chunk from GPU to CPU (async)
+            # Offload this chunk from Compute区 to CPU (async)
             chunk_cpu_blocks = cpu_block_ids[start_block_idx:end_block_idx]
-            offload_engine.offload_buffer_to_cpu(current_buffer, chunk_cpu_blocks)
+            offload_engine.offload_compute_to_cpu(chunk_cpu_blocks)
 
-            # Switch buffer for next chunk
+            # Wait for offload to complete before next chunk
-            if current_buffer == "ping":
+            offload_engine.wait_all_offload_done()
-                offload_engine.wait_ping_offload_done()
-                current_buffer = "pong"
-            else:
-                offload_engine.wait_pong_offload_done()
-                current_buffer = "ping"
 
             processed_tokens = chunk_end
 
         # Wait for all offloads to complete
         offload_engine.wait_all_offload_done()
 
-        print(f"[Ping-Pong Prefill] Complete: {chunk_num} chunks", file=sys.stderr)
+        print(f"[三区域 Prefill] Complete: {chunk_num} chunks", file=sys.stderr)
 
         # Sample from last logits
         temperatures = self.prepare_sample(seqs) if self.rank == 0 else None
@@ -764,25 +776,26 @@ class ModelRunner:
 
     def run_pingpong_decode(self, seqs: list[Sequence]) -> list[int]:
         """
-        Run decode with Ping-Pong dual buffer.
+        Run decode with 三区域 GPU buffer.
 
-        All KV is on CPU. Uses Ping-Pong to load KV chunks and compute attention.
+        All KV is on CPU. Uses Decode区 to write new KV, Compute/Prefetch区 to load KV chunks.
-        New token's KV is written to GPU then offloaded to CPU.
+        New token's KV is written to Decode区 (slot 0) then offloaded to CPU.
+
+        关键：Decode区 永远不会被 Compute/Prefetch 覆盖，专门用于写入新KV。
         """
-        assert len(seqs) == 1, "Ping-Pong decode only supports single sequence"
+        assert len(seqs) == 1, "三区域 decode only supports single sequence"
         seq = seqs[0]
 
+        offload_engine = self.kvcache_manager.offload_engine
+
         # Prepare inputs
         input_ids = torch.tensor([seq.last_token], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
         positions = torch.tensor([len(seq) - 1], dtype=torch.int64, pin_memory=True).cuda(non_blocking=True)
 
-        # Get write slot for new KV (will use last slot of the buffer used for final chunk)
+        # 使用 Decode区 (slot 0) 写入新 KV
-        write_slot = self.kvcache_manager.get_write_slot_for_pingpong(seq)
+        decode_slot = offload_engine.decode_slot  # = 0
-
-        # Calculate position in block for slot mapping
-        last_block_idx = seq.num_blocks - 1
         pos_in_block = (len(seq) - 1) % self.block_size
-        slot = write_slot * self.block_size + pos_in_block
+        slot = decode_slot * self.block_size + pos_in_block
         slot_mapping = torch.tensor([slot], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
         context_len = torch.tensor([len(seq)], dtype=torch.int32, pin_memory=True).cuda(non_blocking=True)
 
@@ -794,17 +807,18 @@ class ModelRunner:
             is_chunked_prefill=True,  # Use chunked attention path
             offload_engine=self.kvcache_manager,
             chunked_seq=seq,
+            decode_pos_in_block=pos_in_block,
         )
 
         # Run model forward pass
         logits = self.run_model(input_ids, positions, is_prefill=False)
         reset_context()
 
-        # Offload new KV from write_slot to CPU
+        # Offload new KV from Decode区 to CPU
         last_cpu_block = self.kvcache_manager.get_last_cpu_block(seq)
         if last_cpu_block >= 0:
-            self.kvcache_manager.offload_engine.offload_slot_to_cpu(write_slot, last_cpu_block)
+            offload_engine.offload_decode_slot(last_cpu_block)
-            self.kvcache_manager.offload_engine.wait_all_offload_done()
+            offload_engine.wait_all_offload_done()
 
         # Sample
         temperatures = self.prepare_sample(seqs) if self.rank == 0 else None

nanovllm/kvcache/__init__.py

@@ -58,12 +58,14 @@ def create_kvcache_manager(config: "Config") -> KVCacheManager:
     from nanovllm.kvcache.policies import get_policy
 
     policy = get_policy(getattr(config, 'offload_policy', 'lru'))
+    num_prefetch_blocks = getattr(config, 'num_prefetch_blocks', 2)
 
     return HybridKVCacheManager(
         num_gpu_slots=num_gpu_blocks,
         num_cpu_blocks=num_cpu_blocks,
         block_size=config.kvcache_block_size,
         policy=policy,
+        num_prefetch_blocks=num_prefetch_blocks,
     )
 
 

nanovllm/kvcache/hybrid_manager.py

@@ -9,6 +9,7 @@ Key design for CUDA Graph compatibility:
 5. Graph replay only needs index updates (tiny overhead)
 """
 
+import logging
 from collections import deque
 from dataclasses import dataclass, field
 from enum import Enum, auto
@@ -16,6 +17,8 @@ from typing import List, Tuple, Dict, Set, Optional
 import torch
 from torch import Tensor
 
+logger = logging.getLogger(__name__)
+
 from nanovllm.engine.sequence import Sequence
 from nanovllm.kvcache.base_manager import KVCacheManager
 from nanovllm.kvcache.offload_engine import OffloadEngine
@@ -82,6 +85,7 @@ class HybridKVCacheManager(KVCacheManager):
         block_size: int,
         policy: Optional[EvictionPolicy] = None,
         cpu_primary: bool = True,
+        num_prefetch_blocks: int = 2,
     ):
         """
         Initialize hybrid manager.
@@ -91,14 +95,16 @@ class HybridKVCacheManager(KVCacheManager):
             num_cpu_blocks: Number of CPU pool blocks (overflow or primary storage)
             block_size: Tokens per block
             policy: Eviction policy (default: LRU)
-            cpu_primary: If True, use CPU as primary storage with Ping-Pong GPU buffer.
+            cpu_primary: If True, use CPU as primary storage with 三区域 GPU buffer.
                         If False, use GPU as primary with CPU as overflow (legacy mode).
+            num_prefetch_blocks: Number of prefetch blocks for 三区域 GPU buffer design
         """
         self._block_size = block_size
         self.num_gpu_slots = num_gpu_slots
         self.num_cpu_blocks = num_cpu_blocks
         self.total_blocks = num_gpu_slots + num_cpu_blocks
-        self.cpu_primary = cpu_primary  # Ping-Pong mode flag
+        self.cpu_primary = cpu_primary  # 三区域 mode flag
+        self.num_prefetch_blocks = num_prefetch_blocks  # 三区域设计参数
 
         # Eviction policy
         self.policy = policy or LRUPolicy()
@@ -156,6 +162,7 @@ class HybridKVCacheManager(KVCacheManager):
             num_kv_heads=num_kv_heads,
             head_dim=head_dim,
             dtype=dtype,
+            num_prefetch_blocks=self.num_prefetch_blocks,
         )
 
     def get_layer_cache(self, layer_id: int) -> Tuple[Tensor, Tensor]:
@@ -948,6 +955,10 @@ class HybridKVCacheManager(KVCacheManager):
                 block = self.logical_blocks[logical_id]
                 if block.location == BlockLocation.CPU:
                     cpu_blocks.append(block.cpu_block_id)
+        logger.debug(
+            f"get_prefilled_cpu_blocks: prefilled_blocks={list(self.prefilled_blocks)}, "
+            f"returned cpu_blocks={cpu_blocks}"
+        )
         return cpu_blocks
 
     def load_prev_kv_for_layer(
@@ -1139,28 +1150,18 @@ class HybridKVCacheManager(KVCacheManager):
 
     def get_write_slot_for_pingpong(self, seq: Sequence) -> int:
         """
-        获取 Ping-Pong decode 时新 KV 写入的 GPU slot。
+        获取三区域 decode 时新 KV 写入的 GPU slot。
 
-        策略：使用序列所需 chunks 数决定最后用的是 Ping 还是 Pong buffer，
+        在三区域设计中，永远使用 Decode区 (slot 0) 写入新 KV。
-        然后使用该 buffer 的最后一个 slot。
+        这样可以避免与 Compute/Prefetch区 的加载操作冲突。
 
         Args:
             seq: 序列
 
         Returns:
-            GPU slot ID
+            GPU slot ID (永远是 decode_slot = 0)
         """
-        cpu_blocks, _ = self.get_all_cpu_blocks(seq)
+        return self.offload_engine.decode_slot
-        ping_size = self.offload_engine.ping_size
-        num_chunks = (len(cpu_blocks) + ping_size - 1) // ping_size if cpu_blocks else 0
-
-        # 最后一个 chunk 用的是哪个 buffer
-        if num_chunks % 2 == 1 or num_chunks == 0:
-            # 奇数个 chunk（或0个），最后用的是 ping
-            return self.offload_engine.ping_slots[-1]
-        else:
-            # 偶数个 chunk，最后用的是 pong
-            return self.offload_engine.pong_slots[-1]
 
     def __repr__(self) -> str:
         return (

nanovllm/kvcache/offload_engine.py

@@ -53,6 +53,7 @@ class OffloadEngine:
         head_dim: int,
         dtype: torch.dtype = torch.float16,
         num_streams: int = 4,
+        num_prefetch_blocks: int = 2,
     ):
         self.num_layers = num_layers
         self.num_gpu_blocks = num_gpu_blocks
@@ -64,31 +65,59 @@ class OffloadEngine:
         self.kv_dim = num_kv_heads * head_dim
         self.block_numel = block_size * self.kv_dim
 
-        # ========== Ping-Pong 双缓冲配置 ==========
+        # ========== 三区域 GPU Buffer 配置 ==========
-        assert num_gpu_blocks >= 2, "Ping-Pong需要至少2个GPU blocks"
+        # 约束检查
-        self.ping_size = num_gpu_blocks // 2
+        assert num_gpu_blocks >= 3, \
-        self.pong_size = num_gpu_blocks - self.ping_size
+            f"至少需要3个GPU blocks: 1 decode + 1 compute + 1 prefetch, got {num_gpu_blocks}"
-        self.ping_slots = list(range(self.ping_size))  # [0, 1, 2, ...]
+        assert num_prefetch_blocks >= 1, \
-        self.pong_slots = list(range(self.ping_size, num_gpu_blocks))  # [ping_size, ping_size+1, ...]
+            f"至少需要1个prefetch block, got {num_prefetch_blocks}"
+        assert num_gpu_blocks >= 1 + 1 + num_prefetch_blocks, \
+            f"GPU blocks不足: 需要 1(decode) + 1(compute) + {num_prefetch_blocks}(prefetch), got {num_gpu_blocks}"
+
+        # 三区域配置
+        # Decode区: [0] - 固定1个block用于写入新KV
+        self.decode_slot = 0
+
+        # Compute区: [1, ..., num_gpu_blocks - num_prefetch_blocks - 1]
+        compute_start = 1
+        compute_end = num_gpu_blocks - num_prefetch_blocks
+        self.compute_slots = list(range(compute_start, compute_end))
+        self.num_compute_blocks = len(self.compute_slots)
+
+        # Prefetch区: [num_gpu_blocks - num_prefetch_blocks, ..., num_gpu_blocks - 1]
+        prefetch_start = compute_end
+        self.prefetch_slots = list(range(prefetch_start, num_gpu_blocks))
+        self.num_prefetch_blocks = num_prefetch_blocks
+
         self.num_gpu_slots = num_gpu_blocks  # alias
 
+        # 保留旧的ping/pong属性以兼容（后续会移除）
+        self.ping_size = self.num_compute_blocks
+        self.pong_size = self.num_prefetch_blocks
+        self.ping_slots = self.compute_slots.copy()
+        self.pong_slots = self.prefetch_slots.copy()
+
+        logger.info(f"三区域 GPU Buffer: decode_slot={self.decode_slot}, "
+                    f"compute_slots={self.compute_slots}, prefetch_slots={self.prefetch_slots}")
+
         # ========== Fixed-address GPU KV cache ==========
         # Shape: [num_layers, num_gpu_blocks, block_size, kv_heads, head_dim]
-        self.k_cache_gpu = torch.empty(
+        # 使用 zeros 初始化以避免未初始化内存问题
+        self.k_cache_gpu = torch.zeros(
             num_layers, num_gpu_blocks, block_size, num_kv_heads, head_dim,
             dtype=dtype, device="cuda"
         )
-        self.v_cache_gpu = torch.empty(
+        self.v_cache_gpu = torch.zeros(
             num_layers, num_gpu_blocks, block_size, num_kv_heads, head_dim,
             dtype=dtype, device="cuda"
         )
 
         # ========== Fixed-address CPU KV cache (pinned memory) ==========
-        self.k_cache_cpu = torch.empty(
+        self.k_cache_cpu = torch.zeros(
             num_layers, num_cpu_blocks, block_size, num_kv_heads, head_dim,
             dtype=dtype, device="cpu", pin_memory=True
         )
-        self.v_cache_cpu = torch.empty(
+        self.v_cache_cpu = torch.zeros(
             num_layers, num_cpu_blocks, block_size, num_kv_heads, head_dim,
             dtype=dtype, device="cpu", pin_memory=True
         )
@@ -111,14 +140,18 @@ class OffloadEngine:
         self.compute_stream = torch.cuda.current_stream()
         self._stream_idx = 0
 
-        # ========== Ping-Pong 专用 stream 和事件 ==========
+        # ========== 三区域专用 stream 和事件 ==========
-        self.pingpong_stream = torch.cuda.Stream()  # 专用于Ping-Pong传输
+        self.transfer_stream_main = torch.cuda.Stream()  # 主传输stream
 
-        # 同步事件 - 加载完成
+        # 同步事件 - 三区域加载完成
-        self.ping_ready = torch.cuda.Event()
+        self.compute_ready = torch.cuda.Event()
-        self.pong_ready = torch.cuda.Event()
+        self.prefetch_ready = torch.cuda.Event()
+        self.decode_offload_done = torch.cuda.Event()
 
-        # 同步事件 - offload完成
+        # 保留旧的ping/pong事件以兼容（后续会移除）
+        self.pingpong_stream = self.transfer_stream_main
+        self.ping_ready = self.compute_ready
+        self.pong_ready = self.prefetch_ready
         self.ping_offload_done = torch.cuda.Event()
         self.pong_offload_done = torch.cuda.Event()
 
@@ -535,7 +568,7 @@ class OffloadEngine:
             f"  kv_heads={self.num_kv_heads},\n"
             f"  head_dim={self.head_dim},\n"
             f"  dtype={self.dtype},\n"
-            f"  ping_size={self.ping_size}, pong_size={self.pong_size},\n"
+            f"  三区域: decode_slot={self.decode_slot}, compute={self.compute_slots}, prefetch={self.prefetch_slots},\n"
             f"  gpu_memory={self.gpu_memory_bytes() / 1024**2:.1f}MB,\n"
             f"  cpu_memory={self.cpu_memory_bytes() / 1024**2:.1f}MB\n"
             f")"
@@ -742,4 +775,189 @@ class OffloadEngine:
         v = self.v_cache_gpu[layer_id, gpu_slots]
         k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
         v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
-        return k, v
+        return k, v
+
+    # ========== 三区域 GPU Buffer 方法 ==========
+
+    def load_to_compute(self, cpu_block_ids: List[int]) -> None:
+        """
+        异步加载CPU blocks到Compute区。
+
+        Args:
+            cpu_block_ids: 要加载的CPU block IDs列表
+        """
+        if not cpu_block_ids:
+            self.compute_ready.record(self.transfer_stream_main)
+            return
+
+        num_to_load = min(len(cpu_block_ids), len(self.compute_slots))
+        logger.debug(f"Compute load: CPU{cpu_block_ids[:num_to_load]} -> GPU compute slots {self.compute_slots[:num_to_load]}")
+
+        with torch.cuda.stream(self.transfer_stream_main):
+            for i in range(num_to_load):
+                cpu_id = cpu_block_ids[i]
+                gpu_slot = self.compute_slots[i]
+                # 所有层一起复制
+                self.k_cache_gpu[:, gpu_slot].copy_(
+                    self.k_cache_cpu[:, cpu_id], non_blocking=True
+                )
+                self.v_cache_gpu[:, gpu_slot].copy_(
+                    self.v_cache_cpu[:, cpu_id], non_blocking=True
+                )
+            self.compute_ready.record(self.transfer_stream_main)
+
+    def load_to_prefetch(self, cpu_block_ids: List[int]) -> None:
+        """
+        异步加载CPU blocks到Prefetch区。
+
+        Args:
+            cpu_block_ids: 要加载的CPU block IDs列表
+        """
+        if not cpu_block_ids:
+            self.prefetch_ready.record(self.transfer_stream_main)
+            return
+
+        num_to_load = min(len(cpu_block_ids), len(self.prefetch_slots))
+        logger.debug(f"Prefetch load: CPU{cpu_block_ids[:num_to_load]} -> GPU prefetch slots {self.prefetch_slots[:num_to_load]}")
+
+        with torch.cuda.stream(self.transfer_stream_main):
+            for i in range(num_to_load):
+                cpu_id = cpu_block_ids[i]
+                gpu_slot = self.prefetch_slots[i]
+                self.k_cache_gpu[:, gpu_slot].copy_(
+                    self.k_cache_cpu[:, cpu_id], non_blocking=True
+                )
+                self.v_cache_gpu[:, gpu_slot].copy_(
+                    self.v_cache_cpu[:, cpu_id], non_blocking=True
+                )
+            self.prefetch_ready.record(self.transfer_stream_main)
+
+    def wait_compute(self) -> None:
+        """等待Compute区加载完成。"""
+        self.compute_stream.wait_event(self.compute_ready)
+
+    def wait_prefetch(self) -> None:
+        """等待Prefetch区加载完成。"""
+        self.compute_stream.wait_event(self.prefetch_ready)
+
+    def swap_compute_prefetch(self) -> None:
+        """交换Compute区和Prefetch区的角色。"""
+        self.compute_slots, self.prefetch_slots = self.prefetch_slots, self.compute_slots
+        # 同时更新旧的ping/pong slots以保持兼容
+        self.ping_slots, self.pong_slots = self.pong_slots, self.ping_slots
+
+    def offload_decode_slot(self, cpu_block_id: int) -> None:
+        """
+        将Decode区的KV offload到CPU。
+
+        Args:
+            cpu_block_id: 目标CPU block ID
+        """
+        logger.debug(f"Decode offload: GPU[{self.decode_slot}] -> CPU[{cpu_block_id}]")
+
+        with torch.cuda.stream(self.transfer_stream_main):
+            self.transfer_stream_main.wait_stream(self.compute_stream)
+            self.k_cache_cpu[:, cpu_block_id].copy_(
+                self.k_cache_gpu[:, self.decode_slot], non_blocking=True
+            )
+            self.v_cache_cpu[:, cpu_block_id].copy_(
+                self.v_cache_gpu[:, self.decode_slot], non_blocking=True
+            )
+            self.decode_offload_done.record(self.transfer_stream_main)
+
+    def wait_decode_offload(self) -> None:
+        """等待Decode区offload完成。"""
+        self.compute_stream.wait_event(self.decode_offload_done)
+
+    def get_kv_for_compute(
+        self,
+        layer_id: int,
+        num_blocks: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取Compute区中指定数量blocks的KV。
+
+        Args:
+            layer_id: 层ID
+            num_blocks: 需要的block数量
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, num_blocks * block_size, kv_heads, head_dim]
+        """
+        slots = self.compute_slots[:num_blocks]
+        k = self.k_cache_gpu[layer_id, slots]  # [num_blocks, block_size, heads, dim]
+        v = self.v_cache_gpu[layer_id, slots]
+        # Reshape: [num_blocks, block_size, heads, dim] -> [1, num_blocks*block_size, heads, dim]
+        k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        return k, v
+
+    def get_kv_for_prefetch(
+        self,
+        layer_id: int,
+        num_blocks: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取Prefetch区中指定数量blocks的KV。
+
+        Args:
+            layer_id: 层ID
+            num_blocks: 需要的block数量
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, num_blocks * block_size, kv_heads, head_dim]
+        """
+        slots = self.prefetch_slots[:num_blocks]
+        k = self.k_cache_gpu[layer_id, slots]
+        v = self.v_cache_gpu[layer_id, slots]
+        k = k.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        v = v.reshape(1, -1, self.num_kv_heads, self.head_dim)
+        return k, v
+
+    def get_kv_for_decode_slot(
+        self,
+        layer_id: int,
+        pos_in_block: int,
+    ) -> Tuple[Tensor, Tensor]:
+        """
+        获取Decode区指定位置的KV（用于decode时的新token）。
+
+        Args:
+            layer_id: 层ID
+            pos_in_block: token在block内的位置 (0 to block_size-1)
+
+        Returns:
+            (k_cache, v_cache)，shape: [1, 1, kv_heads, head_dim]
+        """
+        k = self.k_cache_gpu[layer_id, self.decode_slot, pos_in_block:pos_in_block+1]  # [1, heads, dim]
+        v = self.v_cache_gpu[layer_id, self.decode_slot, pos_in_block:pos_in_block+1]
+        k = k.unsqueeze(0)  # [1, 1, heads, dim]
+        v = v.unsqueeze(0)
+        return k, v
+
+    def offload_compute_to_cpu(self, cpu_block_ids: List[int]) -> None:
+        """
+        将Compute区的KV offload到CPU。
+
+        Args:
+            cpu_block_ids: 目标CPU block IDs列表
+        """
+        if not cpu_block_ids:
+            return
+
+        num_to_offload = min(len(cpu_block_ids), len(self.compute_slots))
+        logger.debug(f"Compute offload: GPU {self.compute_slots[:num_to_offload]} -> CPU{cpu_block_ids[:num_to_offload]}")
+
+        with torch.cuda.stream(self.transfer_stream_main):
+            # 等待计算完成
+            self.transfer_stream_main.wait_stream(self.compute_stream)
+
+            for i in range(num_to_offload):
+                gpu_slot = self.compute_slots[i]
+                cpu_id = cpu_block_ids[i]
+                self.k_cache_cpu[:, cpu_id].copy_(
+                    self.k_cache_gpu[:, gpu_slot], non_blocking=True
+                )
+                self.v_cache_cpu[:, cpu_id].copy_(
+                    self.v_cache_gpu[:, gpu_slot], non_blocking=True
+                )

nanovllm/layers/attention.py

@@ -1,3 +1,4 @@
+import logging
 import torch
 from torch import nn
 import triton
@@ -6,6 +7,8 @@ import triton.language as tl
 from flash_attn.flash_attn_interface import flash_attn_varlen_func, flash_attn_with_kvcache
 from nanovllm.utils.context import get_context
 
+logger = logging.getLogger(__name__)
+
 
 @triton.jit
 def store_kvcache_kernel(
@@ -97,13 +100,16 @@ class Attention(nn.Module):
         context,
     ) -> torch.Tensor:
         """
-        Compute attention with Ping-Pong dual buffer for chunked prefill.
+        Compute attention with 三区域 GPU buffer for chunked prefill.
 
         For chunked prefill:
-        1. Load previous KV from CPU using Ping-Pong (if any previous chunks)
+        1. Load previous KV from CPU using Compute/Prefetch区 (if any previous chunks)
         2. Compute attention against previous KV chunks (no causal mask)
         3. Compute attention against current chunk's KV (causal)
         4. Merge all results using online softmax
+
+        三区域设计保证：当前chunk的KV在Compute区，previous KV从CPU加载到Prefetch区，
+        不会发生写入和加载区域重叠的问题。
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
@@ -116,7 +122,7 @@ class Attention(nn.Module):
         o_acc = None
         lse_acc = None
 
-        # Load previous KV from CPU using Ping-Pong
+        # Load previous KV from CPU using Compute/Prefetch区
         # Note: context.offload_engine is actually HybridKVCacheManager
         kvcache_manager = context.offload_engine
         seq = context.chunked_seq if hasattr(context, 'chunked_seq') else None
@@ -127,41 +133,42 @@ class Attention(nn.Module):
 
             if cpu_block_table:
                 offload_engine = kvcache_manager.offload_engine
-                ping_size = offload_engine.ping_size
+                # 使用 Prefetch区 来加载 previous KV（不会与当前 Compute区 冲突）
-                num_chunks = (len(cpu_block_table) + ping_size - 1) // ping_size
+                prefetch_size = offload_engine.num_prefetch_blocks
-                current_buffer = "ping"
+                num_chunks = (len(cpu_block_table) + prefetch_size - 1) // prefetch_size
+                use_compute = True  # 交替使用 Compute区 和 Prefetch区
 
-                # Prefetch first chunk to Ping buffer
+                # 首先将 previous KV 加载到 Prefetch区
-                first_chunk_end = min(ping_size, len(cpu_block_table))
+                # Only layer 0 triggers the load (loads ALL layers at once)
+                first_chunk_end = min(prefetch_size, len(cpu_block_table))
                 first_chunk_ids = cpu_block_table[:first_chunk_end]
-                offload_engine.load_to_ping(first_chunk_ids)
+                if self.layer_id == 0:
+                    offload_engine.load_to_prefetch(first_chunk_ids)
 
                 for chunk_idx in range(num_chunks):
-                    start = chunk_idx * ping_size
+                    start = chunk_idx * prefetch_size
-                    end = min(start + ping_size, len(cpu_block_table))
+                    end = min(start + prefetch_size, len(cpu_block_table))
                     num_blocks_in_chunk = end - start
 
-                    # Prefetch next chunk to OTHER buffer
+                    # Prefetch next chunk to other buffer (if exists)
-                    if chunk_idx + 1 < num_chunks:
+                    # Only layer 0 triggers the load
+                    if chunk_idx + 1 < num_chunks and self.layer_id == 0:
                         next_start = end
-                        next_end = min(next_start + ping_size, len(cpu_block_table))
+                        next_end = min(next_start + prefetch_size, len(cpu_block_table))
                         next_chunk_ids = cpu_block_table[next_start:next_end]
-                        if current_buffer == "ping":
+                        if use_compute:
-                            offload_engine.load_to_pong(next_chunk_ids)
+                            # 当前在 Prefetch区，下一个加载到 Compute区（如果有空间）
+                            # 注意：Compute区 此时已写入当前chunk的KV，不能覆盖
+                            # 所以这里我们使用简单的同步策略：等待当前完成后再加载
+                            pass  # 简化版本：不进行双缓冲，只用 Prefetch区
                         else:
-                            offload_engine.load_to_ping(next_chunk_ids)
+                            offload_engine.load_to_prefetch(next_chunk_ids)
 
-                    # Wait for current buffer and get KV
+                    # Wait for Prefetch区 and get KV
-                    if current_buffer == "ping":
+                    offload_engine.wait_prefetch()
-                        offload_engine.wait_ping()
+                    prev_k, prev_v = offload_engine.get_kv_for_prefetch(
-                        prev_k, prev_v = offload_engine.get_kv_for_ping_slots(
+                        self.layer_id, num_blocks_in_chunk
-                            self.layer_id, num_blocks_in_chunk
+                    )
-                        )
-                    else:
-                        offload_engine.wait_pong()
-                        prev_k, prev_v = offload_engine.get_kv_for_pong_slots(
-                            self.layer_id, num_blocks_in_chunk
-                        )
 
                     # Compute attention against this chunk (no causal mask)
                     prev_o, prev_lse = flash_attn_with_lse(
@@ -178,8 +185,12 @@ class Attention(nn.Module):
                     else:
                         o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, prev_o, prev_lse)
 
-                    # Switch buffer
+                    # Load next chunk to Prefetch区 (if exists)
-                    current_buffer = "pong" if current_buffer == "ping" else "ping"
+                    if chunk_idx + 1 < num_chunks and self.layer_id == 0:
+                        next_start = end
+                        next_end = min(next_start + prefetch_size, len(cpu_block_table))
+                        next_chunk_ids = cpu_block_table[next_start:next_end]
+                        offload_engine.load_to_prefetch(next_chunk_ids)
 
         # Compute attention against current chunk's KV (with causal mask)
         current_o, current_lse = flash_attn_with_lse(
@@ -207,13 +218,16 @@ class Attention(nn.Module):
         context,
     ) -> torch.Tensor:
         """
-        Compute decode attention with Ping-Pong dual buffer.
+        Compute decode attention with 三区域 GPU buffer.
 
-        All KV is stored on CPU. Uses Ping-Pong buffers on GPU:
+        All KV is stored on CPU. Uses Compute区 buffer on GPU:
-        1. Load first chunk to Ping buffer
+        1. Load chunk to Compute区
-        2. While computing on current buffer, prefetch next chunk to other buffer
+        2. Compute attention
-        3. Alternate between Ping and Pong buffers
+        3. Repeat for all chunks
-        4. Merge attention outputs using online softmax (LSE)
+        4. Finally, attend to Decode区 (slot 0) which contains the new token's KV
+        5. Merge all attention outputs using online softmax (LSE)
+
+        关键：新token的KV在Decode区(slot 0)，不会被Compute区的加载覆盖。
         """
         from nanovllm.kvcache.chunked_attention import flash_attn_with_lse, merge_attention_outputs
 
@@ -227,51 +241,37 @@ class Attention(nn.Module):
 
         # Get all CPU blocks for this sequence
         cpu_block_table, _ = kvcache_manager.get_all_cpu_blocks(seq)
+        if self.layer_id == 0:
+            logger.debug(f"Decode attention: cpu_block_table={cpu_block_table}, seq.block_table={list(seq.block_table)}")
         if not cpu_block_table:
             raise RuntimeError("Chunked decode attention failed: no CPU blocks available")
 
-        # Get the actual offload_engine for Ping-Pong operations
+        # Get the actual offload_engine for 三区域 operations
         offload_engine = kvcache_manager.offload_engine
 
-        # Calculate chunk info
+        # Calculate chunk info using Compute区
-        ping_size = offload_engine.ping_size
+        compute_size = offload_engine.num_compute_blocks
-        num_chunks = (len(cpu_block_table) + ping_size - 1) // ping_size
+        num_chunks = (len(cpu_block_table) + compute_size - 1) // compute_size
 
         o_acc = None
         lse_acc = None
-        current_buffer = "ping"
-
-        # Prefetch first chunk to Ping buffer (loads all layers at once)
-        first_chunk_end = min(ping_size, len(cpu_block_table))
-        first_chunk_ids = cpu_block_table[:first_chunk_end]
-        offload_engine.load_to_ping(first_chunk_ids)
 
         for chunk_idx in range(num_chunks):
-            start = chunk_idx * ping_size
+            start = chunk_idx * compute_size
-            end = min(start + ping_size, len(cpu_block_table))
+            end = min(start + compute_size, len(cpu_block_table))
             num_blocks_in_chunk = end - start
+            chunk_ids = cpu_block_table[start:end]
 
-            # Prefetch next chunk to OTHER buffer (overlapped with current computation)
+            # Load this chunk to Compute区
-            if chunk_idx + 1 < num_chunks:
+            # Only layer 0 triggers the load (loads ALL layers at once)
-                next_start = end
+            if self.layer_id == 0:
-                next_end = min(next_start + ping_size, len(cpu_block_table))
+                offload_engine.load_to_compute(chunk_ids)
-                next_chunk_ids = cpu_block_table[next_start:next_end]
-                if current_buffer == "ping":
-                    offload_engine.load_to_pong(next_chunk_ids)
-                else:
-                    offload_engine.load_to_ping(next_chunk_ids)
 
-            # Wait for current buffer to be ready and get KV
+            # Wait for Compute区 to be ready and get KV
-            if current_buffer == "ping":
+            offload_engine.wait_compute()
-                offload_engine.wait_ping()
+            k_chunk, v_chunk = offload_engine.get_kv_for_compute(
-                k_chunk, v_chunk = offload_engine.get_kv_for_ping_slots(
+                self.layer_id, num_blocks_in_chunk
-                    self.layer_id, num_blocks_in_chunk
+            )
-                )
-            else:
-                offload_engine.wait_pong()
-                k_chunk, v_chunk = offload_engine.get_kv_for_pong_slots(
-                    self.layer_id, num_blocks_in_chunk
-                )
 
             # Compute attention for this chunk
             o_chunk, lse_chunk = flash_attn_with_lse(
@@ -286,8 +286,21 @@ class Attention(nn.Module):
             else:
                 o_acc, lse_acc = merge_attention_outputs(o_acc, lse_acc, o_chunk, lse_chunk)
 
-            # Switch buffer for next iteration
+        # Now attend to Decode区 (contains the new token's KV)
-            current_buffer = "pong" if current_buffer == "ping" else "ping"
+        # This is the token being decoded - only 1 token at position pos_in_block
+        pos_in_block = context.decode_pos_in_block
+        decode_k, decode_v = offload_engine.get_kv_for_decode_slot(self.layer_id, pos_in_block)
+        decode_o, decode_lse = flash_attn_with_lse(
+            q_batched, decode_k, decode_v,
+            softmax_scale=self.scale,
+            causal=False,
+        )
+
+        # Merge with accumulated
+        if o_acc is None:
+            o_acc = decode_o
+        else:
+            o_acc, _ = merge_attention_outputs(o_acc, lse_acc, decode_o, decode_lse)
 
         if o_acc is None:
             raise RuntimeError("Chunked decode attention failed: no KV available")

nanovllm/utils/context.py

@@ -27,6 +27,8 @@ class Context:
     prev_kv_chunks: List[Tuple[torch.Tensor, torch.Tensor]] = field(default_factory=list)
     # Current sequence being processed (for chunked prefill to load KV)
     chunked_seq: Any = None
+    # Position within block for decode (used for reading from Decode区)
+    decode_pos_in_block: int = 0
 
 
 _CONTEXT = Context()
@@ -50,6 +52,7 @@ def set_context(
     chunk_offset=0,
     offload_engine=None,
     chunked_seq=None,
+    decode_pos_in_block=0,
 ):
     global _CONTEXT
     _CONTEXT = Context(
@@ -66,6 +69,7 @@ def set_context(
         chunk_offset=chunk_offset,
         offload_engine=offload_engine,
         chunked_seq=chunked_seq,
+        decode_pos_in_block=decode_pos_in_block,
     )